The present invention, in some embodiments thereof, relates to motion detection, and more particularly, but not exclusively, to a system useful for identifying gait or fall related motion.
A public health issue of concern is the incidence of falls, in which a person falls to the ground from an upright position while standing or walking. The problem of falls affects the elderly in general, and is of particular concern for older persons and others who have a movement disorder or other illness that affects balance and motor control, such as Parkinson's disease.
The effect of a fall on an elderly person can be particularly serious since many elderly people have weak or brittle bones, and are generally further weakened by other illnesses and the effects of aging. In some cases a fall causes the death of a person, either at the time of the fall or indirectly as a result of the injuries sustained. The type of injuries commonly experienced may include one or more of: a broken or fractured hip and other bones, head injuries, internal and external bleeding, and soft tissue and skin damage. The patient will most likely suffer a great deal of pain and may require hospitalization. In addition, he or she may face the prospect of long term or permanent loss of mobility, since their age and condition may mean that the injuries will take a long time to heal or may never heal completely. The patient may be plagued by fear of a recurrence, so that their mobility and confidence is further compromised. Accordingly, even if death is avoided, the injuries suffered from a fall can be devastating to the person's physical and mental well-being.
Various systems have been proposed to automatically identify falls, so that an action can be triggered to help alleviate the damage caused by the fall. For example, upon detecting that a fall has occurred, a system could notify a relative or doctor to check up on the patient. Dinh et al. in “A Fall Detection and Near-Fall Data Collection System” (Microsystems and Nanoelectronics Research Conference (MNRC), October 2008) describe a wearable device containing a 3-axis accelerometer, a 2-axis gyroscope, and a heart beat detection circuit. Data collected from the sensors is beamed wirelessly to a receiver connected to a computer. The researchers observed that combining the accelerometer data with the gyroscope data produced good results in identifying whether a fall had occurred.
Bourke et al. in “Distinguishing Falls from Normal ADL using Vertical Velocity Profiles”, (IEEE Conference on Engineering in Medicine and Biology, August 2007) observe that a single threshold applied to the vertical velocity profile of the trunk may distinguish falls from activities of daily living (ADL).
In another paper, Wu and Xue in “Portable Preimpact Fall Detector With Inertial Sensors” (IEEE Transactions on Neural Systems and Rehabilitation Engineering, April 2008), describe a portable preimpact fall detector that detects a pending fall at its inception, so that an inflatable hip protector can be triggered in time to break the fall. The detector was equipped with an orientation or inertial sensor that included triaxial accelerometers and triaxial angular rate sensors, and used a detection algorithm based on the inertial frame velocity profile of the body. In particular, the inertial frame vertical velocity magnitude was measured and compared to a threshold value to identify a fall. The system was tested in a variety of activities to determine the threshold level of inertial frame vertical velocity magnitude.